The addition of true color in an image or video can provide more information to a human observer or computer processor than a panchromatic luminance-only image. This additional color information can assist in making critical decisions. For example, image understanding and target discrimination are improved when the color information provided is comparable to the color seen by the human eye (“true color”) as opposed to taking spectral bands not seen by the human eye and mapping them on the display as colors (“false color”).
When capturing images under low light conditions, such as those occurring at night, the number of photons in the visible spectrum is limited and therefore the signal-to-noise ratio (SNR) in an image captured by an image sensor sensitive in the visible spectrum is inherently limited. If a color image is captured by the image sensor, the number of photons is further reduced since the light incident on a single sensor element is filtered to remove all the photons that are not within the bandpass of the filter. For example, a red filter blocks all photons in the green and blue portions of the spectrum. Thus, the SNR of a true color night vision image or video is further degraded compared to a panchromatic night vision image or video.
Silicon-based image sensors, such as sensors fabricated using complementary metal-oxide semiconductor (CMOS) processes, capture photons in the visible and the near infrared (NIR) spectral bands unless there is a spectral filter blocking a portion of these bands. Under typical night conditions, the majority of ambient light is in the near infrared band compared with that in the visible band. Thus for a silicon sensor with color filters and an NIR blocking filter, there is a greater degradation of image quality due to the smaller signal level and therefore smaller SNR of a color night vision image captured with a silicon-based image sensor compared to a visible +NIR panchromatic image captured with that same silicon-based image sensor.